Machine Learning Engineer

We are looking for a Senior Machine Learning Engineer with deep expertise in Transformers, Large Language Models (LLMs), and Natural Language Processing (NLP). This role involves designing, training, and fine-tuning state-of-the-art AI models for real-world applications. The ideal candidate will have a strong research background and hands-on experience in deploying scalable NLP solutions.

Key Responsibilities

• Research, develop, and optimize Transformer-based architectures (e.g., BERT, GPT, T5, LLaMA) for various NLP tasks.
• Fine-tune LLMs on domain-specific datasets to improve accuracy and performance.
• Work on text generation, summarization, named entity recognition (NER), and semantic search applications.
• Implement and optimize embedding techniques for retrieval-augmented generation (RAG).
• Apply self-supervised and reinforcement learning techniques to enhance model performance.
• Deploy and scale ML models using cloud platforms (AWS, GCP, Azure) and containerized solutions like Docker and Kubernetes.
• Improve inference efficiency using quantization, distillation, and model optimization techniques.
• Collaborate with data engineers, software developers, and research scientists to integrate ML models into production.
• Stay updated with the latest advancements in AI, NLP, and Deep Learning, applying innovative techniques to solve business challenges.

Required Skills & Qualifications

• Expertise in NLP & LLMs: Strong understanding of transformer-based models (e.g., BERT, GPT, T5, LLaMA).
• Programming Skills: Proficiency in Python and deep learning frameworks like PyTorch, TensorFlow, and Hugging Face Transformers.
• Model Optimization: Experience with quantization, pruning, and distillation to improve model efficiency.
• Data Handling: Strong experience in preprocessing, tokenization, and vectorization of large text datasets.
• Deployment & Scalability: Hands-on experience with MLOps, API development, cloud services (AWS, GCP, Azure), and containerization (Docker, Kubernetes).
• Information Retrieval & RAG: Knowledge of vector databases (FAISS, Pinecone, Weaviate) and embedding techniques.
• Mathematical Foundation: Strong background in linear algebra, probability, and deep learning architectures.
• Collaboration: Ability to work with cross-functional teams and communicate technical concepts effectively.

Preferred Qualifications

• Experience in low-rank adaptation (LoRA) and fine-tuning LLMs with limited resources.
• Exposure to multimodal learning (text, images, audio).
• Research publications or contributions to open-source NLP projects.
• Familiarity with prompt engineering and fine-tuning for AI assistants.

What We Offer

• Opportunity to work on cutting-edge AI and NLP projects with a talented team.
• Ability to shape the development of next-generation AI applications.
• Access to latest ML research, conferences, and learning resources.
• Flexible work arrangements (remote/hybrid options available).
• Competitive salary and performance-based incentives.